Route Navigation Research Articles

Book Your Green Wave: Exploiting Navigation Information for Intelligent Traffic Signal Control

Traffic congestion alleviation around intersections has been a growing challenge, and a competent traffic signal control scheme plays a pivotal role in intelligent transportation systems. Recent studies using deep reinforcement learning techniques have shown promising results for traffic signal control, but they only focus on extracting features from traffic conditions of isolated or adjacent intersections. In this work, we employed navigation information for traffic signal control, greatly enriching the features for traffic signal control with deep reinforcement learning. In addition, we are the first to propose a novel scheme DeepNavi to exploit the temporal-spatial relations from numerous navigation routes and extract dynamic real-time and future traffic features. We tested our scheme on a challenging real-world traffic dataset with 16 intersections in a residential district of Hangzhou, China. Extensive experiments were conducted and the results demonstrated that our DeepNavi scheme achieves superior performance over five popular and state-of-the-art baseline methods on different metrics, including queue length, speed, travel time and accumulative waiting time. In addition, with our method, vehicles suffer the least red lights and enjoy the most green waves, which further validates that our scheme greatly relieves the congestion and provides excellent experience for drivers. Simulations with different penetration levels of navigation routes showed that even with only part of navigation routes available in the traffic network, our scheme can obtain superior performance, further demonstrating the effectiveness and feasibility of DeepNavi.

Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning.

This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.

Optimization of safe navigability conditions in shallow inland waters: The Routinav app

Safe navigation in inland waters subjected to tide requires dedicated forecasts of meteorological and oceanographic variables, such as local tidal height, currents, wind and atmospheric pressure. Indeed, it is more difficult to navigate inland shallow waters subjected to intense morphologic dynamics, and therefore characterized by the non-linearity of the tidal height propagation throughout the channels, traduced by important changes in tidal amplitude and phase, especially when no navigation aids are installed. In this context, this work objective is a mobile application (Routinav) development that allows simulating the navigability conditions in inland shallow waters. Firstly, a monitoring system was installed on volunteer boats and validated in Ria de Aveiro, showing high accuracy in depth and position acquisition. Secondly, was designed and applied an algorithm allowing the identification of navigable and non-navigable route segments, according to the updated depth and forecasted tidal height by the hydrodynamic model. Based on this algorithm, was created an agile prototype used to build up the Routinav mobile application for iOS (iPhone OS) and Android operating systems.

Mapping the thickness of sea ice in the Arctic as an example of using data from a ship-based television complex for operational hydrometeorological support of maritime activities

Sea ice charts produced by the ice services of the world are among the most widely used sources of information about sea ice conditions in the Arctic. The absolute majority of sea ice charts are based on visual expert analysis of satellite imagery accompanied by auxiliary data including ground information from coastal stations and ships navigating the Northern Sea Route (NSR). Ground measurements of sea ice thickness are necessary for validating the results of satellite imagery interpretation. Shipboard observations are highly valuable because, unlike coastal stations, the ships provide information about sea ice cover straight on the navigational routes, not in the coastal areas of land-fast ice, where the thickness values are not fully representative of the ice in the open sea. However, the current system of shipboard observations used by commercial fleets often does not meet the reliability requirements due to the human factor involved in the process of data collection. In the early 2000s, the Arctic and Antarctic Research Institute (AARI) suggested a new methodology for shipboard ice thickness measurement. A ship-based television complex (STC) was developed in order to exclude the human factor and standardize observations. The inaccuracy value was estimated as 3.8 % of the real thickness. By 2018, STC had been upgraded to a new ship-based television meteorological complex (STMC) allowing continuous automatic measurement of ice thickness and many other related hydrometeorological parameters during the entire voyage. The automatic and autonomous operation of the new equipment allows placing it on board the ship without the need for an ice specialist to be permanently present. It means that STMC can be used by commercial fleets, which constantly increase the number of Arc7 ice class vessels they use. For economic reasons, reinforced ice class vessels, whose number is growing, represent the only available infrastructure suitable for the deployment of distributed network providing operational hydrometeorological monitoring on the NSR. A comparison of STC data with AARI ice charts has revealed that real-time transmission of STC data from ships to the ice service office could increase the accuracy of ice charts and, as a consequence, the quality of the entire system of hydrometeorological informational support of maritime activities in the Arctic.

Vision-based mapping of lane semantics and topology for intelligent vehicles

High-definition map is an essential tool for route measurement, planning and navigation of intelligent vehicles. Yet its creation is still a persisting challenge, especially in creating the semantic and topology layer of the map based on visual sensing. However, current semantic mapping approaches do not consider the map applicability in navigation tasks while the topology mapping approaches face the issues of limited location accuracy or expensive hardware cost. In this paper, we propose a joint mapping framework for both semantic and topology layers, which are learned in a lane-level and based on a monocular camera sensor and an on-board GPS positioning device. A map management approach “RoadSegDict” is also proposed to support the efficient updating of semantic map in a crowdsourced manner. Moreover, a new dataset is proposed, which includes a variety of lane structures with detailed semantic and topology annotations.

Little Lewis and Latin Folk in Chaucer's Prologue to the Treatise on the Astrolabe

Little Lewis and Latin Folk in Chaucer's Prologue to the <i>Treatise on the Astrolabe</i>

МОДЕЛЬ ФОРМИРОВАНИЯ СУДОХОДНЫХ МАРШРУТОВ В АКВАТОРИИ СЕВЕРНОГО МОРСКОГО ПУТИ

This work is devoted to developing a generalized model for forming shipping routes in the waters of the Arctic seas. The research theme is related to solving the strategic task of providing a year-round navigation in the waters of the Northern Sea Route. The characteristics of the navigation-hydrographic and ice conditions for performing the maritime Arctic transport system are given. The article presents the main indicators characterizing the volume of cargo transportation carried out along the routes of the Northern Sea Route in 2021 and the number of ships operating in the water area. The main factors influencing the network development of year-round shipping routes are determined. An analytical review of known models and methods for assessing navigational risks are carried out. A list of the main factors of navigational risks is established. A generalized model is proposed for joint considering the influence of shallow depths, ice conditions and tightness of the water area on the navigation safety. The main formulae, correlations and limitations of the model, as well as generalized and particular criteria for indicators of navigational safety are given. The results of checking the model performance are presented. The possibility of a year-round navigation of two-type large-capacity vessels in the Sannikov Strait is considered. Applying the model makes it possible to perform a comparative assessment of the ship navigation routes according to the generalized criterion of navigational safety and particular criteria. A technique is developed for the practical use of the proposed model to solve the problems of ensuring safe navigation in the conditions of extended and a year-round navigation in the waters of the Northern Sea Route. The article outlines the ways of further research

Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision

The pushing robot working in the complex farming environment encounters several problems. For example, the precision of its navigation path extraction is low, and its working quality is greatly affected by the weather. In view of this, a method of farm operation path extraction based on machine vision is proposed in this study in order to solve the problems above and realize the autonomous and intelligent operation of the robot. First of all, the RGB images of the working area in front of the robot are obtained by using an RGB camera installed on the machine. Then, the collected images are preprocessed by means of sky removal, denoising and grayscale transformation. After that, the image is segmented to obtain the front fence, feed belt and ground data. Finally, the navigation path is obtained by extracting the features of the feed belt. The test results show that the absolute deviation of the pushing robot at different initial lateral distances is less than ±15 cm, and the deviation between the actual navigation route and the target route is within the expected range. The absolute value of the maximum lateral deviation in five test areas is 8.9 cm, and the absolute value of the average maximum lateral deviation is 7.6 cm. These experimental results show that the pushing robot can work stably without disturbing the feeding of cows. Particle swarm optimization is used to optimize the parameters of the PID and find the optimal parameters. This makes the system balanced and more responsive. Through this test, it is found that the initial direction of the robot will have a certain impact on the path production and tracking efficiency, and this effect is more significant when the robot changes the working area or turns. In which case, the trajectory of the robot should be in such a way that it immediately faces the next row at a small angular deviation, thus ensuring smoother motion. The method proposed in this study can provide support for the automatic navigation of pushing robots in dairy farms.

Smart Wheelchair Based on IoT for Disabled and Elderly People

Abstract: Pervasive devices are becoming an integral part of people's daily lives. Smart gadgets make people's lives easier and are becoming an essential part of the lives of physically tested and matured people. The need for safe and unrestricted mobility for the elderly and physically challenged is critical. The study deals with the creation of an Intelligent Wheel-Chair (IWC) that focuses on a certain issue in an exceptionally moderate and prominent manner.Wheel-Chair is controlled by RTOS as its center working framework. It comprises of touchscreen based route navigation alongside obstacle avoidance and fall identification. As a result with voice command movement of the wheel chair is dependent which will help disable and old aged people. The Node MCU used successfully detected the commanded and he Arduino micro-controller used in this project has successfully delivered its purpose.

Travel Time Estimation by Learning Driving Habits and Traffic Conditions

Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the complex relationships of these factors for TTE. To fill this gap, in this paper, we first analyze how these factors work together in determining the travel time. In particular, the travel time depends on the distance and driving speed on each road segment of the trajectory, where the driving speed depends on the driving habits and the environment, including the static factors like the road type (highway or byway) and speed limit and the dynamic factor like the time of the day and congestion. Among these factors, driving habits and traffic conditions (e.g., jam) are the most difficult ones to model. Second, we propose to learn the driving habits of each driver via meta-learning and estimate the conditions based on the current and historical traffic conditions (via recurrent neural networks) of this road and its connected road segments (via graph convolutional neural network). The experimental results on two real taxi trajectory datasets show that our approach outperforms three state-of-the-art methods significantly.

OPTIMAL SPLINE TRAJECTORY OF THE SHIP INFORMATIVE ROUTE IN THE MAP-AIDED NAVIGATION

The task of organizing an informative route as an optimal spline trajectory of a moving object with an assessment of the informativeness of the map-aided navigation standard is completed. From the perspective of the planning approach, an optimal geometric path, passing through pre-determined iconic intermediate points, taking into account the avoidance of navigational hazards as “obstacle spots” has been formed. Within the framework of the strategy of informative planning of the spline path, the actuality of solving the problem of synthesizing the optimal trajectory in two variants is noted: by the methods of B-splines and classical polynomial interpolations as the implementation of the tactics of a mobile object movement in a conflict environment. A comparative characteristic of two alternative algorithms for solving the problem, specifying the advantages and disadvantages of each option, is given. As a demonstration of the practical applicability of the interpolation approach, the spline trajectory of an illustrative example in route of map-aided navigation is designed against the background of a contour map of isolines. Emphasis is placed on the possibility of forming the shape of the navigation isosurface due to the effective use of the curvature of the spline trajectory as a reproductive template for constructing an axonometric projection. A forecast about the trends of the possible use of a separate optimal trajectory of the object movement directly for the construction of the informative field profile of any degree of complexity is made. A hypothesis about the feasibility of practical use of chaotic architecture of spline gradients for effective planning of the optimal trajectory is put forward. The fan of spline gradient vectors with a personal orientation in the direction of the maximum change in the navigation function on each segment of the piecewise polyline of the path in the vessel routing procedure is considered. The issue of ensuring the possibility of making a coordinated decision on the vessel management by personnel due to the automated formation of spline trajectories in real time with synchronous representation of geometric computer support to the watch assistant, which allows us to offer integration of the tasks under consideration into the cloud-based intelligent technology of “augmented reality” is formulated.

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information can also be gathered along with ordinary RGB color data. Using these RGB-D sensors, two- and three-dimensional point clouds can be recorded from the mobile devices, which provide additional information for localization and mapping. The method of matching point clouds during the movement of the device is essential: reducing noise while having an acceptable processing time is crucial for a real-life application. In this paper, we present a novel ISVD-based method for displacement estimation, using key points detected by SURF and ORB feature detectors. The ISVD algorithm is a fitting procedure based on SVD resolution, which removes outliers from the point clouds to be fitted in several steps. The developed method removes these outlying points in several steps, in each iteration examining the relative error of the point pairs and then progressively reducing the maximum error for the next matching step. An advantage over relevant methods is that this method always gives the same result, as no random steps are included.

A graph-rotation model for optimal ship routing in ice conditions

Ship independent navigation in ice-infested waters is a risky operation. However, it is widely practiced in the Northern Baltic and other freezing regions. Up to now, there is no conventional algorithmic approach, which provides robust and efficient routes for winter navigation. In the article, we propose a new graph method for algorithmic finding of a route in the complicated environment. This method is based on a concept of local grid rotation and provides optimal routes in sense of its length and number of direction changes. Unlike Dijkstra and A*, the new method allows finding a short route with minimum number of zig-zags, which ensures distance from shores. The presented method is computationally rather efficient and employs extended wave algorithm which has been developed earlier by the authors. We provide the case study of finding an efficient route in ice conditions of the Baltic Sea given by an ice chart. Calculations are performed for a vessel with Ice2 class travelling to Gulf of Bothnia. The route found by the new method is compared with the globally shortest path evaluated by Dijkstra's algorithm on a graph with advanced grid. The new algorithm provided much smoother route with nearly the same travel distance.

Development of Modern Transport System in the Bengal Delta and British Borneo: A Comparative Environmental Perspective, 1850-1963

This paper presents comparative studies of modern transportation systems in the Bengal Delta and British Borneo. To meet the demands of the new modes of resource extraction, the British colonial rulers introduced a new transportation system in both regions and built roads, railways, and navigational routes connecting major commercial and political centers. There has been little research into the historical connections between modern transportation and environmental changes in colonial South Asia and Malaysia. When modern transportation was introduced, environmental consequences were rarely considered. As a result, significant ecological changes and declines were unintentionally caused. The environmental changes brought about by these transportation systems in these two regions were not the same one from the other. For example, railroad construction harmed the plains and waterways in the Bengal Delta, whereas, in British Borneo, rubber plantations for the global market harmed the rainforests.

&amp;quot;Landmark Route&amp;quot;: A Comparison to the Shortest Route

Abstract. Most navigation systems for pedestrians output the shortest route. However, there are findings that travellers do not use the shortest route when free to choose. One alternative to minimising spatial distance is the incorporation of landmark information in a shortest route algorithm. Yet, we do not know whether pedestrians prefer such a landmark route over the shortest route. Therefore, we perform a survey and show participants videos of a shortest and a landmark route. We let participants answer questions concerning navigation satisfaction, route communication, and route comparison. Our findings show that the landmark route is more favourable.

Do people prefer a landmark route over a shortest route?

ABSTRACT Landmarks play an important role in spatial cognition, and therefore should be considered as a part of pedestrian navigation. This research addressed the question of human preference for landmark routes that are generated based on landmarks over shortest-distance optimized routes. We investigated whether there are differences regarding navigation behavior, navigation satisfaction, route memorization, route communication, and route comparison between the shortest and the landmark route. We did not observe any differences for navigation behavior, route memorization, and route communication. We observed that for the landmark route the participants were significantly faster in reversing the route. Generally, we found out that the participants were more satisfied with the shortest route and preferred the shortest route. We found hints that this might result from environmental parameters, which seemed to have an impact on navigation satisfaction. Additionally, the participants perceived the routes to be longer than they really were.

JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism

Accurate travel time prediction is one of the most promising intelligent transportation system (ITS) services, which can greatly support route planning, ride-sharing, navigation applications, and effective traffic management. Several factors, like spatial, temporal, and external, have big effects on traffic patterns, and therefore, it is important to develop a mechanism that can jointly capture correlations of these components. However, spatial sparsity issues make travel time prediction very challenging, especially when dealing with the origin-destination (OD) method, since the trajectory data may not be available. In this paper, we introduce a unified deep learning-based framework named joint spatial-temporal correlation (JSTC) mechanism to improve the accuracy of OD travel time prediction. First, we design a spatiotemporal correlation block that combines two modules: self-convolutional attention integrated with a temporal convolutional network (TCN) to capture the spatial correlations along with the temporal dependencies. Then, we enhance our model performance through adopting a multi-head attention module to learn the attentional weights of the spatial, temporal, and external features based on their contributions to the output and speed up the training process. Extensive experiments on three large-scale real-world traffic datasets (NYC, Chengdu, and Xi’an) show the efficiency of our model and its superiority compared to other methods.

Spatial Analysis for Mariculture Site Selection: A Case Study of Kukup Aquaculture Zones in the Peninsula of Malaysia

Marine aquaculture sites at Kukup Strait in the peninsula of Malaysia are experiencing a decline in water quality in the last five years, resulting in high mortality rates (50 - 80%), seasonal massive fish mortalities and poor profitability. Currently, Kukup farmers are involved in intensive aquaculture within the existing aquaculture zone. This study explores the sustainability of a modern mariculture method, where fish farms are relocated to deeper waters with higher flushing rates, and high-density polyethylene (HDPE) are used to improve water quality. Several site selection criteria are utilised in identifying the proposed aquaculture industrial zone: 1) bathymetry depth (operational requirement: 10 m to 30 m), 2) located within the Kukup Port Limit, 3) does not interfere with the Kukup ferry navigation route, and 4) distance from Kukup mainland. Site feasibility analysis, including hydrodynamics, meteorology, water quality, sediment quality and macrobenthos assessment, was conducted to compare the proposed and existing aquaculture sites. It was found that the water quality at the existing site contained higher concentrations of fecal coliform and nutrients and salinity fluctuations. Tropical finfish can survive under these conditions with poorer fish health and higher fish mortality. Apart from that, the flushing capacity is higher in the proposed site, indicating the capacity to handle moderate-intensity aquaculture. Moderate-intensity aquaculture using HDPE cages could be profitable for the local fish farmers. Therefore, it is suggested that other locations within Kukup Straits with deeper waters (&amp;gt; 25 m) with possibly improved water quality and the ability for intensive aquaculture production should be explored for a larger depth and economics of scale.

Extreme Waves

The occurrence of exceptionally large waves in regions of high maritime traffic has severe consequences, ranging from complicating navigation routes to the loss of ships and human lives [...]

Ship Path Planning Based on Buoy Offset Historical Trajectory Data

In the existing research on the intelligent navigation of ships, navigation route planning often regards light buoys as fixed obstructions. However, due to factors such as water ripples, the position of the buoys keeps periodically changing. If the buoys are set to a fixed range of avoidance areas in the process of ship navigation, it is easy to allow a collision between the ship and the light buoys. Therefore, based on historical motion trajectory data of the buoys, a SARIMA-based time-series prediction model is proposed to estimate the offset position of a given buoy in a specified time. Furthermore, the collision-free path planning approach is presented to dynamically recommend an accurate sailing path. The results of the simulation experiment show that this method can effectively deal with collisions of ships caused by the offset position of the light buoys during the navigation of the large and low-speed autonomous ships.